Fix conversion of OneHot for dtypes unsupported by ORT

Signed-off-by: Tom Wildenhain <tomwi@microsoft.com>

tests/test_backend.py

@@ -2271,13 +2271,15 @@ def func(x):
                 return tf.identity(x_, name=_TFOUTPUT)
             self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
-    @check_target("rs6", "onehot")
+    @skip_tfjs("tfjs produces incorrect results")
     def test_onehot0(self):
         x_val = np.array([0, 1, 2], dtype=np.int32)
         depth = 5
-        for axis in [-1, 0, 1]:
+        for dtype, axis in [(tf.float32, -1), (tf.int64, 0), (tf.float64, 1)]:
             def func(x):
-                x_ = tf.one_hot(x, depth, on_value=5.0, axis=axis, off_value=1.0, dtype=tf.float32)
+                val1 = tf.constant(5, dtype)
+                val2 = tf.constant(1, dtype)
+                x_ = tf.one_hot(x, depth, on_value=val1, axis=axis, off_value=val2, dtype=dtype)
                 return tf.identity(x_, name=_TFOUTPUT)
             self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 

tf2onnx/onnx_opset/tensor.py

@@ -1313,8 +1313,11 @@ def version_1(cls, ctx, node, **kwargs):
         if axis.i == 0:
             # TODO: revisit for rank > 1
             name = utils.make_name(node.name)
-            transpose_node = ctx.insert_new_node_on_output("Transpose", node.output[0], name)
-            ctx.copy_shape(node.output[0], transpose_node.output[0])
+            shape = ctx.get_shape(node.output[0])
+            transpose_node = ctx.make_node("Transpose", [node.output[0]], name=name, shapes=[shape])
+            ctx.insert_node_on_output(transpose_node, node.output[0])
+            if shape is not None:
+                ctx.set_shape(node.output[0], shape[::-1])
 
     @classmethod
     def any_version_after9(cls, opset, ctx, node, **kwargs):
@@ -1323,9 +1326,11 @@ def any_version_after9(cls, opset, ctx, node, **kwargs):
         # in ONNX, op's schema is (input, depth, value, @int axis), meaning of "value" is [off-value, on-value]
         # onnxruntime only supports int64
         output_dtype = ctx.get_dtype(node.input[2])
-        if ctx.is_target(constants.TARGET_RS6) \
-                and output_dtype not in [onnx_pb.TensorProto.INT64, onnx_pb.TensorProto.INT32]:
-            logger.warning("unsupported dtype in onnxruntime, onehot-9 can't be used directly")
+        supported_dtypes = [onnx_pb.TensorProto.FLOAT]
+        if ctx.is_target(constants.TARGET_RS6):
+            supported_dtypes = [onnx_pb.TensorProto.INT64, onnx_pb.TensorProto.INT32]
+        if output_dtype not in supported_dtypes:
+            logger.warning("unsupported dtype in target runtime, OneHot op can't be used directly")
             cls.version_1(ctx, node, **kwargs)
             return